hotel_stays %>% plot_bar(maxcat = 50, nrow = 3)
4 columns ignored with more than 50 categories.
country: 166 categories
agent: 315 categories
company: 332 categories
reservation_status_date: 805 categories

skim(hotel_stays)
── Data Summary ────────────────────────
                           Values     
Name                       hotel_stays
Number of rows             75166      
Number of columns          29         
_______________________               
Column type frequency:                
  character                14         
  Date                     1          
  numeric                  14         
________________________              
Group variables            None       

── Variable type: character ──────────────────────────────────────────────────────────────────────────────────────────────────────
   skim_variable               n_missing complete_rate   min   max empty n_unique whitespace
 1 hotel                               0             1    10    12     0        2          0
 2 arrival_date_month                  0             1     3     9     0       12          0
 3 children                            0             1     4     8     0        2          0
 4 meal                                0             1     2     9     0        5          0
 5 country                             0             1     2     4     0      166          0
 6 market_segment                      0             1     6    13     0        7          0
 7 distribution_channel                0             1     3     9     0        5          0
 8 reserved_room_type                  0             1     1     1     0        9          0
 9 assigned_room_type                  0             1     1     1     0       10          0
10 deposit_type                        0             1    10    10     0        3          0
11 agent                               0             1     1     4     0      315          0
12 company                             0             1     1     4     0      332          0
13 customer_type                       0             1     5    15     0        4          0
14 required_car_parking_spaces         0             1     4     7     0        2          0

── Variable type: Date ───────────────────────────────────────────────────────────────────────────────────────────────────────────
  skim_variable           n_missing complete_rate min        max        median     n_unique
1 reservation_status_date         0             1 2015-07-01 2017-09-14 2016-09-01      805

── Variable type: numeric ────────────────────────────────────────────────────────────────────────────────────────────────────────
   skim_variable                  n_missing complete_rate      mean     sd      p0    p25    p50   p75  p100 hist 
 1 lead_time                              0             1   80.0    91.1      0       9     45     124   737 ▇▂▁▁▁
 2 arrival_date_year                      0             1 2016.      0.703 2015    2016   2016    2017  2017 ▃▁▇▁▆
 3 arrival_date_week_number               0             1   27.1    13.9      1      16     28      38    53 ▆▇▇▇▆
 4 arrival_date_day_of_month              0             1   15.8     8.78     1       8     16      23    31 ▇▇▇▇▆
 5 stays_in_weekend_nights                0             1    0.929   0.993    0       0      1       2    19 ▇▁▁▁▁
 6 stays_in_week_nights                   0             1    2.46    1.92     0       1      2       3    50 ▇▁▁▁▁
 7 adults                                 0             1    1.83    0.510    0       2      2       2     4 ▁▂▇▁▁
 8 is_repeated_guest                      0             1    0.0433  0.204    0       0      0       0     1 ▇▁▁▁▁
 9 previous_cancellations                 0             1    0.0158  0.272    0       0      0       0    13 ▇▁▁▁▁
10 previous_bookings_not_canceled         0             1    0.203   1.81     0       0      0       0    72 ▇▁▁▁▁
11 booking_changes                        0             1    0.293   0.736    0       0      0       0    21 ▇▁▁▁▁
12 days_in_waiting_list                   0             1    1.59   14.8      0       0      0       0   379 ▇▁▁▁▁
13 adr                                    0             1  100.0    49.2     -6.38   67.5   92.5   125   510 ▇▆▁▁▁
14 total_of_special_requests              0             1    0.714   0.834    0       0      1       1     5 ▇▁▁▁▁

막대그래프

막대 그래프

hotel_stays %>%
  mutate(arrival_date_month = factor(arrival_date_month,
    levels = month.name
  )) %>%
  count(hotel, arrival_date_month, children) %>%
  group_by(hotel, children) %>%
  mutate(proportion = n / sum(n)) %>%
  plot_ly(x = ~arrival_date_month , y = ~proportion, color = ~children, type = "bar") %>%
  layout(title = "Occupation Type Group",
         barmode = 'group',
         xaxis = list(title = ""),
         yaxis = list(title = ""))
minimal value for n is 3, returning requested palette with 3 different levels
minimal value for n is 3, returning requested palette with 3 different levels
minimal value for n is 3, returning requested palette with 3 different levels
minimal value for n is 3, returning requested palette with 3 different levels

데이터 한판에 보기

산점도 그래프

하이라이트 적용

---
title: "R Notebook"
output: html_notebook

---
<style type="text/css">

body, td {
   font-size: 12px;
}
code.r{
  font-size: 12px;
}
pre {
  font-size: 12px
}
</style>

```{r set up ,include=FALSE}
library(knitr)
knitr::opts_chunk$set(cache = T, warning = F,
                      message = F, echo = T, dpi = 180,
                      fig.width = 8, fig.height = 5)

theme_set(theme_bw() +
         theme(plot.title = element_text(size = 15, face = "bold"),
               plot.subtitle = element_text(size = 12, face = "bold"),
               axis.text.x = element_text(size = 8),
               axis.text.y = element_text(size = 8),
               axis.title.x = element_text(size = 12),
               axis.title.y = element_text(size = 12),
               legend.text =element_text(size = 12),
               legend.title =element_text(size = 12)
              )
          )

# tidymodels
for (package in c('janitor','skimr', 'correlationfunnel', 'DataExplorer')) {
  if (!require(package, character.only=T, quietly=T)) {
    install.packages(package)
    library(package, character.only=T)
  }
}

# tidymodels
for (package in c('gghighlight', 'patchwork', 'ggplot2', 'GGally', 'highcharter','correlationfunnel','plotly')) {
  if (!require(package, character.only=T, quietly=T)) {
    install.packages(package)
    library(package, character.only=T)
  }
}

# Core
for (package in c('doParallel', 'tidyverse', 'tidyquant', 'knitr')) {
  if (!require(package, character.only=T, quietly=T)) {
    install.packages(package)
    library(package, character.only=T)
  }
}
all_cores <- parallel::detectCores(logical = FALSE)
registerDoParallel(cores = all_cores)
```


```{r}

hotels <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-02-11/hotels.csv") %>%
  clean_names()
  # select(순서,everthing())

hotel_stays <- hotels %>%
  filter(is_canceled == 0) %>%
  mutate(
    children = case_when(
      children + babies > 0 ~ "children",
      TRUE ~ "none"
    ),
    required_car_parking_spaces = case_when(
      required_car_parking_spaces > 0 ~ "parking",
      TRUE ~ "none"
    )
  ) %>%
  select(-is_canceled, -reservation_status, -babies)

hotel_stays
# kable(head(hotel_stays), caption = "table with kable") #깔끔한 테이블 표현 (양 적을때)
```

```{r}
hotel_stays %>% plot_missing()
```

```{r}
hotel_stays %>% plot_bar(maxcat = 50, nrow = 3)
```

```{r}
hotel_stays %>% plot_histogram()
```

```{r}
skim(hotel_stays)
```

```{r}
hotel_stays %>%
  count(children) %>%
  plot_ly(labels = ~children, values = ~n) %>%
  add_pie(hole = 0.6) %>%
  layout(title = "subject",  showlegend = T,
         xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
         yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))

```


## 막대그래프

```{r fig.height=5, fig.width=10}
hotel_stays %>%
  mutate(arrival_date_month = factor(arrival_date_month,
    levels = month.name
  )) %>%
  count(hotel, arrival_date_month, children) %>%
  group_by(hotel, children) %>%
  mutate(proportion = n / sum(n)) %>%
  # plot_ly( x = ~arrival_date_month , y = ~proportion, color = ~children,type = 'bar', name = 'aa') +
  # facet_wrap(~hotel, ncol= 5, scales = "free")
  ggplot(aes(arrival_date_month, proportion, fill = children)) +
  geom_col(position = "dodge") +
  scale_y_continuous(labels = scales::percent_format()) +
  facet_wrap(~hotel, nrow = 2) +
  labs(
    x = NULL,
    y = "Proportion of hotel stays",
    fill = NULL) +
  ggplot2::theme_light()
# a
# ggplotly(a)
  # )
```


## 막대 그래프
```{r}

colorsPuYe <- c("#5C374C", "#985277", "#CE6A85", "#FFCF6A", "#FFB742", "#E9692C")

hotel_stays %>%
  count(hotel, required_car_parking_spaces, children) %>%
  group_by(hotel, children) %>%
  mutate(proportion = n / sum(n)) %>%
  ggplot(aes(required_car_parking_spaces, proportion, fill = children)) +
  geom_col(position = "dodge") +
  scale_y_continuous(labels = scales::percent_format()) +
  facet_wrap(~hotel, nrow = 2) +
  labs(
    x = NULL,
    y = "Proportion of hotel stays",
    fill = NULL
  ) +
  coord_flip() +
  geom_label(aes(label = round(proportion,2)), size = 3, position=position_dodge(width=0.9))
  # scale_fill_gradient(low=colorsPuYe[3], high=colorsPuYe[1], guide = "none")

```

```{r}
hotel_stays %>%
  mutate(arrival_date_month = factor(arrival_date_month,
    levels = month.name
  )) %>%
  count(hotel, arrival_date_month, children) %>%
  group_by(hotel, children) %>%
  mutate(proportion = n / sum(n)) %>%
  plot_ly(x = ~arrival_date_month , y = ~proportion, color = ~children, type = "bar") %>%
  layout(title = "Occupation Type Group",
         barmode = 'group',
         xaxis = list(title = ""),
         yaxis = list(title = ""))

```

```{r}
trace_0 <- rnorm(100, mean = 5)
trace_1 <- rnorm(100, mean = 0)
trace_2 <- rnorm(100, mean = -5)
x <- c(1:100)

data <- data.frame(x, trace_0, trace_1, trace_2)

fig <- plot_ly(data, x = ~x, y = ~trace_0, name = 'trace 0', type = 'scatter', mode = 'lines') 
fig <- fig %>% add_trace(y = ~trace_1, name = 'trace 1', mode = 'lines+markers') 
fig <- fig %>% add_trace(y = ~trace_2, name = 'trace 2', mode = 'markers')

fig
```

## 데이터 한판에 보기
```{r}
hotel_stays %>%
  select(
    children, adr,
    required_car_parking_spaces,
    total_of_special_requests
  ) %>%
  ggpairs(mapping = aes(color = children))
```

# 산점도 그래프
```{r}
hotel_stays %>%
    ggplot(aes(lead_time, adr)) +
    geom_point(color = palette_light()["blue"], alpha = 0.15) +
    geom_smooth(method = "lm") +
    scale_y_log10(label = scales::dollar_format()) +
    theme_tq() 
    # labs(title = "Engine Horsepower vs MSRP by Vehicle Age",
    #      x = "Engine HP", y = "MSRP (log scale)")
```

## 하이라이트 적용
```{r}
hotel_stays %>%
    ggplot(aes(lead_time, adr)) +
    geom_point(color = palette_light()["blue"], alpha = 0.15) +
    geom_smooth(method = "lm") +
    scale_y_log10(label = scales::dollar_format()) +
    gghighlight(lead_time > 600, label_key = lead_time, 
                unhighlighted_colour = alpha("grey", 0.05),
                label_params = list(size = 2.5)) +

    theme_tq() 
    # labs(title = "Engine Horsepower vs MSRP by Vehicle Age",
    #      x = "Engine HP", y = "MSRP (log scale)")
```

```{r}
# select_if(is.numeric) 숫자형만

hotel_stays %>% select(-reservation_status_date) %>% binarize(n_bins = 5, thresh_infreq = 0.01 , name_infreq = "OUTHER", one_hot = T) %>%
    correlate(lead_time__5_27) %>%
    plot_correlation_funnel()


```


```{r}
hotel_stays %>% ggpairs(columns=c(2,7,26), aes(colour=children, alpha=0.4),
                 lower=list(continuous=wrap("smooth", method="loess", se=F)))
```